<th class="col_heading level0 col0" >Total</th> <th class="col_heading level0 col1" >Percent</th> </tr></thead><tbody>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row0" class="row_heading level0 row0" >Cabin</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row0_col0" class="data row0 col0" >687</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row0_col1" class="data row0 col1" >77.10%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row1" class="row_heading level0 row1" >Age</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row1_col0" class="data row1 col0" >177</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row1_col1" class="data row1 col1" >19.87%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row2" class="row_heading level0 row2" >Embarked</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row2_col0" class="data row2 col0" >2</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row2_col1" class="data row2 col1" >0.22%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row3" class="row_heading level0 row3" >Fare</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row3_col0" class="data row3 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row3_col1" class="data row3 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row4" class="row_heading level0 row4" >Ticket</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row4_col0" class="data row4 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row4_col1" class="data row4 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row5" class="row_heading level0 row5" >Parch</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row5_col0" class="data row5 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row5_col1" class="data row5 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row6" class="row_heading level0 row6" >SibSp</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row6_col0" class="data row6 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row6_col1" class="data row6 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row7" class="row_heading level0 row7" >Sex</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row7_col0" class="data row7 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row7_col1" class="data row7 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row8" class="row_heading level0 row8" >Name</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row8_col0" class="data row8 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row8_col1" class="data row8 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row9" class="row_heading level0 row9" >Pclass</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row9_col0" class="data row9 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row9_col1" class="data row9 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row10" class="row_heading level0 row10" >Survived</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row10_col0" class="data row10 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row10_col1" class="data row10 col1" >0.00%</td>
</tr>
<tr>
<th id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1level0_row11" class="row_heading level0 row11" >PassengerId</th>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row11_col0" class="data row11 col0" >0</td>
<td id="T_87693f78_94e5_11ea_8771_4d40f2ce22e1row11_col1" class="data row11 col1" >0.00%</td>
</tr>
</tbody></table></td></tr></table>
{% endraw %}
{% raw %}
Train set missing values.
|
|
|
|
PassengerId
|
Survived
|
Pclass
|
Name
|
Sex
|
Age
|
SibSp
|
Parch
|
Ticket
|
Fare
|
Cabin
|
Embarked
|
|
counts
|
891
|
891
|
891
|
891
|
891
|
714
|
891
|
891
|
891
|
891
|
204
|
889
|
|
uniques
|
891
|
2
|
3
|
891
|
2
|
88
|
7
|
7
|
681
|
248
|
147
|
3
|
|
missing
|
0
|
0
|
0
|
0
|
0
|
177
|
0
|
0
|
0
|
0
|
687
|
2
|
|
missing_perc
|
0%
|
0%
|
0%
|
0%
|
0%
|
19.87%
|
0%
|
0%
|
0%
|
0%
|
77.10%
|
0.22%
|
|
types
|
numeric
|
bool
|
numeric
|
unique
|
bool
|
numeric
|
numeric
|
numeric
|
categorical
|
numeric
|
categorical
|
categorical
|
{% endraw %}
{% raw %}
|
|
passengerid
|
pclass
|
name
|
sex
|
age
|
sibsp
|
parch
|
ticket
|
fare
|
cabin
|
embarked
|
survived
|
|
0
|
1
|
3
|
Braund, Mr. Owen Harris
|
male
|
22.0
|
1
|
0
|
A/5 21171
|
7.2500
|
NaN
|
S
|
0
|
|
1
|
2
|
1
|
Cumings, Mrs. John Bradley (Florence Briggs Th…
|
female
|
38.0
|
1
|
0
|
PC 17599
|
71.2833
|
C85
|
C
|
1
|
|
2
|
3
|
3
|
Heikkinen, Miss. Laina
|
female
|
26.0
|
0
|
0
|
STON/O2. 3101282
|
7.9250
|
NaN
|
S
|
1
|
|
3
|
4
|
1
|
Futrelle, Mrs. Jacques Heath (Lily May Peel)
|
female
|
35.0
|
1
|
0
|
113803
|
53.1000
|
C123
|
S
|
1
|
|
4
|
5
|
3
|
Allen, Mr. William Henry
|
male
|
35.0
|
0
|
0
|
373450
|
8.0500
|
NaN
|
S
|
0
|
{% endraw %}
{% raw %}
mean 32.2042
std 49.6934
variance 2469.44
min 0
max 512.329
mode 8.05
5% 7.225
25% 7.9104
50% 14.4542
75% 31
95% 112.079
iqr 23.0896
kurtosis 33.3981
skewness 4.78732
sum 28693.9
mad 28.1637
cv 1.54307
zeros_num 15
zeros_perc 1.68%
deviating_of_mean 20
deviating_of_mean_perc 2.24%
deviating_of_median 53
deviating_of_median_perc 5.95%
top_correlations
counts 891
uniques 248
missing 0
missing_perc 0%
types numeric
Name: fare, dtype: object
{% endraw %}
{% raw %}
Summarize dataset: 100%|██████████| 26/26 [00:04<00:00, 6.31it/s, Completed]
Generate report structure: 100%|██████████| 1/1 [00:01<00:00, 1.94s/it]
Render HTML: 100%|██████████| 1/1 [00:00<00:00, 1.07it/s]
{% endraw %}
Easily view the histogram of multiple features.
{% raw %}
{% endraw %}
Create a configurable correlation matrix.
{% raw %}
<matplotlib.axes._subplots.AxesSubplot at 0x7fd038322748>
{% endraw %}
We can easily plot the average price each age paid for a ticket.
{% raw %}
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG"></script><script type="text/javascript">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}</script>
<script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<div id="baf0b49c-b8da-45e6-9426-35cf7a811c78" class="plotly-graph-div" style="height:525px; width:100%;"></div>
<script type="text/javascript">
window.PLOTLYENV=window.PLOTLYENV || {};
if (document.getElementById("baf0b49c-b8da-45e6-9426-35cf7a811c78")) {
Plotly.newPlot(
'baf0b49c-b8da-45e6-9426-35cf7a811c78',
[{"alignmentgroup": "True", "hoverlabel": {"namelength": 0}, "hovertemplate": "Age=%{x}<br>Fare=%{y}", "legendgroup": "", "marker": {"color": "#636efa"}, "name": "", "offsetgroup": "", "orientation": "v", "showlegend": false, "textposition": "auto", "type": "bar", "x": ["0.92", "64.0", "58.0", "35.0", "50.0", "38.0", "36.0", "49.0", "43.0", "46.0", "60.0", "11.0", "53.0", "52.0", "15.0", "54.0", "56.0", "63.0", "24.0", "14.0", "71.0", "70.0", "41.0", "18.0", "23.0", "48.0", "2.0", "42.0", "40.0", "31.0", "45.0", "39.0", "62.0", "65.0", "7.0", "21.0", "27.0", "1.0", "80.0", "37.0", "44.0", "4.0", "51.0", "17.0", "8.0", "9.0", "19.0", "47.0", "29.0", "10.0", "36.5", "33.0", "3.0", "16.0", "6.0", "30.0", "22.0", "25.0", "32.0", "61.0", "0.83", "55.0", "5.0", "32.5", "28.0", "0.75", "26.0", "45.5", "34.0", "0.67", "14.5", "13.0", "28.5", "57.0", "12.0", "40.5", "66.0", "59.0", "20.0", "0.42", "55.5", "24.5", "30.5", "74.0", "70.5", "20.5", "23.5", "34.5"], "xaxis": "x", "y": [151.55, 144.5, 93.90166000000002, 89.31249999999999, 64.02583, 62.751509090909096, 59.96495909090909, 59.929183333333334, 59.7975, 55.458333333333336, 55.0, 54.240625, 51.4792, 51.40278333333333, 49.65501999999999, 44.477087499999996, 43.976025, 43.7729, 43.03569, 42.6257, 42.0792, 40.75, 39.18888333333334, 38.06346153846153, 37.994719999999994, 37.89306666666667, 37.53625, 37.125646153846155, 37.10993076923077, 37.00907058823529, 36.81840833333334, 36.661899999999996, 35.9, 32.093066666666665, 31.6875, 31.56562083333333, 30.361338888888895, 30.005957142857138, 30.0, 29.811116666666667, 29.75833333333334, 29.543329999999997, 28.752385714285715, 28.389423076923077, 28.3, 27.938537500000002, 27.869496000000005, 27.60138888888889, 27.090825000000002, 26.025, 26.0, 25.82555333333334, 25.78195, 25.745099999999997, 25.583333333333332, 25.541668, 25.50478148148148, 24.415765217391304, 24.323377777777782, 24.019433333333335, 23.875, 23.25, 22.7177, 21.5354, 21.020159999999997, 19.2583, 19.086805555555557, 17.8625, 16.63638666666667, 14.5, 14.4542, 13.3646, 11.6646, 11.425, 11.2417, 11.125, 10.5, 10.375, 8.624173333333333, 8.5167, 8.05, 8.05, 7.9, 7.775, 7.75, 7.25, 7.2292, 6.4375], "yaxis": "y"}],
{"barmode": "relative", "legend": {"tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "white", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "white", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "#C8D4E3", "linecolor": "#C8D4E3", "minorgridcolor": "#C8D4E3", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, 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We can also easily view the relationship between age and fair and see the difference between those who survived and who didn’t.
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{% endraw %}
You can visualize other plots like raincloud, violin, box, pairwise, etc. I recommend checking out the examples for more!
One of the big changes is that ability to work with pandas side by side. If you want to transform and work with data solely with Pandas, the Analysis object will reflect those changes. This allows you to use Aethos solely for automated analysis and Pandas for transformations.
To demonstrate this we will make a new boolean feature to see if a passenger was a child using the original pandas dataframe we created
{% raw %}
|
|
passengerid
|
survived
|
pclass
|
name
|
sex
|
age
|
sibsp
|
parch
|
ticket
|
fare
|
cabin
|
embarked
|
is_child
|
|
0
|
1
|
0
|
3
|
Braund, Mr. Owen Harris
|
male
|
22.0
|
1
|
0
|
A/5 21171
|
7.2500
|
NaN
|
S
|
0
|
|
1
|
2
|
1
|
1
|
Cumings, Mrs. John Bradley (Florence Briggs Th…
|
female
|
38.0
|
1
|
0
|
PC 17599
|
71.2833
|
C85
|
C
|
0
|
|
2
|
3
|
1
|
3
|
Heikkinen, Miss. Laina
|
female
|
26.0
|
0
|
0
|
STON/O2. 3101282
|
7.9250
|
NaN
|
S
|
0
|
|
3
|
4
|
1
|
1
|
Futrelle, Mrs. Jacques Heath (Lily May Peel)
|
female
|
35.0
|
1
|
0
|
113803
|
53.1000
|
C123
|
S
|
0
|
|
4
|
5
|
0
|
3
|
Allen, Mr. William Henry
|
male
|
35.0
|
0
|
0
|
373450
|
8.0500
|
NaN
|
S
|
0
|
{% endraw %}
Now let’s see it in our Analysis object.
{% raw %}
|
|
passengerid
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age
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sibsp
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parch
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is_child
|
|
0
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1
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0
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3
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Braund, Mr. Owen Harris
|
male
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22.0
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1
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0
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0
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1
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2
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1
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1
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Cumings, Mrs. John Bradley (Florence Briggs Th…
|
female
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38.0
|
1
|
0
|
PC 17599
|
71.2833
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C85
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C
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0
|
|
2
|
3
|
1
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3
|
Heikkinen, Miss. Laina
|
female
|
26.0
|
0
|
0
|
STON/O2. 3101282
|
7.9250
|
NaN
|
S
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0
|
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3
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4
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1
|
1
|
Futrelle, Mrs. Jacques Heath (Lily May Peel)
|
female
|
35.0
|
1
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0
|
113803
|
53.1000
|
C123
|
S
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0
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|
4
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5
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0
|
3
|
Allen, Mr. William Henry
|
male
|
35.0
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0
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0
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373450
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8.0500
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NaN
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S
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0
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"ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "white", "showlakes": true, "showland": true, "subunitcolor": "#C8D4E3"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": {"angularaxis": {"gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": ""}, "bgcolor": "white", "radialaxis": {"gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8"}, "yaxis": {"backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8"}, "zaxis": {"backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": ""}, "baxis": {"gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": ""}, "bgcolor": "white", "caxis": {"gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "title": {"text": "is_child"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "fare"}}},
{"responsive": true}
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</div>
{% endraw %}
You can still run pandas functions on Aethos objects.
{% raw %}
passengerid 891
survived 2
pclass 3
name 891
sex 2
age 88
sibsp 7
parch 7
ticket 681
fare 248
cabin 147
embarked 3
is_child 2
dtype: int64
{% endraw %}
{% raw %}
{% endraw %}
Introduced in Aethos 2.0 are some new analytic techniques.
The predictive power score is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two columns. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). It can be used as an alternative to the correlation (matrix). Credits go to 8080Labs for creating this library and you can get more info here
{% raw %}
<matplotlib.axes._subplots.AxesSubplot at 0x7fd0384a2198>
{% endraw %}
AutoViz auto visualizes your data and displays key plots based off the characteristics of your data. Credits go to AutoViML for creating this library and you can get more info here.
{% raw %}
Imported AutoViz_Class version: 0.0.68. Call using:
from autoviz.AutoViz_Class import AutoViz_Class
AV = AutoViz_Class()
AutoViz(filename, sep=',', depVar='', dfte=None, header=0, verbose=0,
lowess=False,chart_format='svg',max_rows_analyzed=150000,max_cols_analyzed=30)
To remove previous versions, perform 'pip uninstall autoviz'
Shape of your Data Set: (891, 13)
Classifying variables in data set...
12 Predictors classified...
This does not include the Target column(s)
4 variables removed since they were ID or low-information variables
Total Number of Scatter Plots = 3
Nothing to add Plot not being added
All plots done
Time to run AutoViz (in seconds) = 2.659
{% endraw %}
Aethos 2.0 introduces 3 new model objects: Classification, Regression and Unsupervised. These objects have the same capabilities of the Analysis object, but also can transform your data the same way it did in Aethos 1.0. For those new to Aethos, whenever you use Aethos to apply a transformation, it fits it to the training data and applies it to both the training and test data (in the case of Classification and Regression) to avoid data leakage.
In this post we’ll cover the Classification object but the process is the exact same if you were working with a Regression or Unsupervised problem.
{% raw %}
{% endraw %}
As with Aethos 1.0 if no test data is provided, it is split upon initialization. In Aethos 2.0 it uses stratification for classification problems to split the data to ensure some resemblance of class balance.
{% include warning.html content=‘Earlier we showed the ability to alter the original dataframe and have it reflected in the Aethos object. This is NOT the case if you do not provide a test set for the Classification and Regression object.’ %}
{% raw %}
|
|
PassengerId
|
Survived
|
Pclass
|
Name
|
Sex
|
Age
|
SibSp
|
Parch
|
Ticket
|
Fare
|
Cabin
|
Embarked
|
|
count
|
668
|
668
|
668
|
NaN
|
NaN
|
533
|
668
|
668
|
NaN
|
668
|
NaN
|
NaN
|
|
mean
|
441.913
|
0.383234
|
2.29192
|
NaN
|
NaN
|
29.4192
|
0.510479
|
0.377246
|
NaN
|
32.4659
|
NaN
|
NaN
|
|
std
|
260.048
|
0.486539
|
0.841285
|
NaN
|
NaN
|
14.7713
|
1.08757
|
0.781087
|
NaN
|
51.5116
|
NaN
|
NaN
|
|
min
|
1
|
0
|
1
|
NaN
|
NaN
|
0.42
|
0
|
0
|
NaN
|
0
|
NaN
|
NaN
|
|
25%
|
214.75
|
0
|
1.75
|
NaN
|
NaN
|
20
|
0
|
0
|
NaN
|
7.925
|
NaN
|
NaN
|
|
50%
|
450.5
|
0
|
3
|
NaN
|
NaN
|
28
|
0
|
0
|
NaN
|
14.4542
|
NaN
|
NaN
|
|
75%
|
668.25
|
1
|
3
|
NaN
|
NaN
|
38
|
1
|
0
|
NaN
|
31.275
|
NaN
|
NaN
|
|
max
|
891
|
1
|
3
|
NaN
|
NaN
|
80
|
8
|
5
|
NaN
|
512.329
|
NaN
|
NaN
|
|
counts
|
668
|
668
|
668
|
668
|
668
|
533
|
668
|
668
|
668
|
668
|
160
|
666
|
|
uniques
|
668
|
2
|
3
|
668
|
2
|
82
|
7
|
6
|
545
|
216
|
121
|
3
|
|
missing
|
0
|
0
|
0
|
0
|
0
|
135
|
0
|
0
|
0
|
0
|
508
|
2
|
|
missing_perc
|
0%
|
0%
|
0%
|
0%
|
0%
|
20.21%
|
0%
|
0%
|
0%
|
0%
|
76.05%
|
0.30%
|
|
types
|
numeric
|
bool
|
numeric
|
unique
|
bool
|
numeric
|
numeric
|
numeric
|
categorical
|
numeric
|
categorical
|
categorical
|
{% endraw %}
{% raw %}
|
|
PassengerId
|
Survived
|
Pclass
|
Name
|
Sex
|
Age
|
SibSp
|
Parch
|
Ticket
|
Fare
|
Cabin
|
Embarked
|
|
0
|
482
|
0
|
2
|
Frost, Mr. Anthony Wood “Archie”
|
male
|
NaN
|
0
|
0
|
239854
|
0.0000
|
NaN
|
S
|
|
1
|
828
|
1
|
2
|
Mallet, Master. Andre
|
male
|
1.0
|
0
|
2
|
S.C./PARIS 2079
|
37.0042
|
NaN
|
C
|
|
2
|
562
|
0
|
3
|
Sivic, Mr. Husein
|
male
|
40.0
|
0
|
0
|
349251
|
7.8958
|
NaN
|
S
|
|
3
|
865
|
0
|
2
|
Gill, Mr. John William
|
male
|
24.0
|
0
|
0
|
233866
|
13.0000
|
NaN
|
S
|
|
4
|
283
|
0
|
3
|
de Pelsmaeker, Mr. Alfons
|
male
|
16.0
|
0
|
0
|
345778
|
9.5000
|
NaN
|
S
|
{% endraw %}
{% raw %}
|
|
PassengerId
|
Survived
|
Pclass
|
Name
|
Sex
|
Age
|
SibSp
|
Parch
|
Ticket
|
Fare
|
Cabin
|
Embarked
|
|
0
|
187
|
1
|
3
|
O’Brien, Mrs. Thomas (Johanna “Hannah” Godfrey)
|
female
|
NaN
|
1
|
0
|
370365
|
15.5000
|
NaN
|
Q
|
|
1
|
321
|
0
|
3
|
Dennis, Mr. Samuel
|
male
|
22.0
|
0
|
0
|
A/5 21172
|
7.2500
|
NaN
|
S
|
|
2
|
379
|
0
|
3
|
Betros, Mr. Tannous
|
male
|
20.0
|
0
|
0
|
2648
|
4.0125
|
NaN
|
C
|
|
3
|
698
|
1
|
3
|
Mullens, Miss. Katherine “Katie”
|
female
|
NaN
|
0
|
0
|
35852
|
7.7333
|
NaN
|
Q
|
|
4
|
509
|
0
|
3
|
Olsen, Mr. Henry Margido
|
male
|
28.0
|
0
|
0
|
C 4001
|
22.5250
|
NaN
|
S
|
{% endraw %}
{% raw %}
<th class="col_heading level0 col0" >Total</th> <th class="col_heading level0 col1" >Percent</th> </tr></thead><tbody>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row0" class="row_heading level0 row0" >Cabin</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row0_col0" class="data row0 col0" >508</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row0_col1" class="data row0 col1" >76.05%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row1" class="row_heading level0 row1" >Age</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row1_col0" class="data row1 col0" >135</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row1_col1" class="data row1 col1" >20.21%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row2" class="row_heading level0 row2" >Embarked</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row2_col0" class="data row2 col0" >2</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row2_col1" class="data row2 col1" >0.30%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row3" class="row_heading level0 row3" >Fare</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row3_col0" class="data row3 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row3_col1" class="data row3 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row4" class="row_heading level0 row4" >Ticket</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row4_col0" class="data row4 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row4_col1" class="data row4 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row5" class="row_heading level0 row5" >Parch</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row5_col0" class="data row5 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row5_col1" class="data row5 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row6" class="row_heading level0 row6" >SibSp</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row6_col0" class="data row6 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row6_col1" class="data row6 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row7" class="row_heading level0 row7" >Sex</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row7_col0" class="data row7 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row7_col1" class="data row7 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row8" class="row_heading level0 row8" >Name</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row8_col0" class="data row8 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row8_col1" class="data row8 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row9" class="row_heading level0 row9" >Pclass</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row9_col0" class="data row9 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row9_col1" class="data row9 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row10" class="row_heading level0 row10" >Survived</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row10_col0" class="data row10 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row10_col1" class="data row10 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665758_94ef_11ea_8771_4d40f2ce22e1level0_row11" class="row_heading level0 row11" >PassengerId</th>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row11_col0" class="data row11 col0" >0</td>
<td id="T_01665758_94ef_11ea_8771_4d40f2ce22e1row11_col1" class="data row11 col1" >0.00%</td>
</tr>
</tbody></table></td><td style='padding-right:25px'><style type="text/css" >
Train set missing values.
|
|
<th class="col_heading level0 col0" >Total</th> <th class="col_heading level0 col1" >Percent</th> </tr></thead><tbody>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row0" class="row_heading level0 row0" >Cabin</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row0_col0" class="data row0 col0" >179</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row0_col1" class="data row0 col1" >80.27%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row1" class="row_heading level0 row1" >Age</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row1_col0" class="data row1 col0" >42</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row1_col1" class="data row1 col1" >18.83%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row2" class="row_heading level0 row2" >Embarked</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row2_col0" class="data row2 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row2_col1" class="data row2 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row3" class="row_heading level0 row3" >Fare</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row3_col0" class="data row3 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row3_col1" class="data row3 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row4" class="row_heading level0 row4" >Ticket</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row4_col0" class="data row4 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row4_col1" class="data row4 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row5" class="row_heading level0 row5" >Parch</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row5_col0" class="data row5 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row5_col1" class="data row5 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row6" class="row_heading level0 row6" >SibSp</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row6_col0" class="data row6 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row6_col1" class="data row6 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row7" class="row_heading level0 row7" >Sex</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row7_col0" class="data row7 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row7_col1" class="data row7 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row8" class="row_heading level0 row8" >Name</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row8_col0" class="data row8 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row8_col1" class="data row8 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row9" class="row_heading level0 row9" >Pclass</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row9_col0" class="data row9 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row9_col1" class="data row9 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row10" class="row_heading level0 row10" >Survived</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row10_col0" class="data row10 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row10_col1" class="data row10 col1" >0.00%</td>
</tr>
<tr>
<th id="T_01665759_94ef_11ea_8771_4d40f2ce22e1level0_row11" class="row_heading level0 row11" >PassengerId</th>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row11_col0" class="data row11 col0" >0</td>
<td id="T_01665759_94ef_11ea_8771_4d40f2ce22e1row11_col1" class="data row11 col1" >0.00%</td>
</tr>
</tbody></table></td></tr></table>
{% endraw %}
{% include tip.html content=‘Aethos comes with a checklist to help give you reminders when cleaning, analyzing and transforming your data!’ %}
{% raw %}
{% endraw %}
{% raw %}
|
|
passengerid
|
pclass
|
name
|
sex
|
age
|
sibsp
|
parch
|
ticket
|
fare
|
cabin
|
embarked
|
survived
|
|
0
|
482
|
2
|
Frost, Mr. Anthony Wood “Archie”
|
male
|
NaN
|
0
|
0
|
239854
|
0.0000
|
NaN
|
S
|
0
|
|
1
|
828
|
2
|
Mallet, Master. Andre
|
male
|
1.0
|
0
|
2
|
S.C./PARIS 2079
|
37.0042
|
NaN
|
C
|
1
|
|
2
|
562
|
3
|
Sivic, Mr. Husein
|
male
|
40.0
|
0
|
0
|
349251
|
7.8958
|
NaN
|
S
|
0
|
|
3
|
865
|
2
|
Gill, Mr. John William
|
male
|
24.0
|
0
|
0
|
233866
|
13.0000
|
NaN
|
S
|
0
|
|
4
|
283
|
3
|
de Pelsmaeker, Mr. Alfons
|
male
|
16.0
|
0
|
0
|
345778
|
9.5000
|
NaN
|
S
|
0
|
{% endraw %}
Since this is an overview, let’s select the columns were going to work with and drop the ones we’re not going to use.
{% raw %}
|
|
pclass
|
sex
|
age
|
fare
|
embarked
|
survived
|
|
0
|
2
|
male
|
NaN
|
0.0000
|
S
|
0
|
|
1
|
2
|
male
|
1.0
|
37.0042
|
C
|
1
|
|
2
|
3
|
male
|
40.0
|
7.8958
|
S
|
0
|
|
3
|
2
|
male
|
24.0
|
13.0000
|
S
|
0
|
|
4
|
3
|
male
|
16.0
|
9.5000
|
S
|
0
|
{% endraw %}
Let’s chain our transformations together. Remember our transformations will be fit to the training data and automatically transform our test data!
{% raw %}
Pandas Apply: 100%|██████████| 668/668 [00:00<00:00, 77148.31it/s]
Pandas Apply: 100%|██████████| 223/223 [00:00<00:00, 57466.81it/s]
|
|
sex_female
|
sex_male
|
pclass_1
|
pclass_2
|
pclass_3
|
embarked_C
|
embarked_Q
|
embarked_S
|
is_child
|
fare
|
age
|
survived
|
|
0
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0.0
|
0.0
|
1.0
|
0
|
0.000000
|
0.346569
|
0
|
|
1
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1
|
0.072227
|
0.007288
|
1
|
|
2
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0
|
0.015412
|
0.497361
|
0
|
|
3
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0.0
|
0.0
|
1.0
|
0
|
0.025374
|
0.296306
|
0
|
|
4
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
1
|
0.018543
|
0.195778
|
0
|
{% endraw %}
{% raw %}
|
|
sex_female
|
sex_male
|
pclass_1
|
pclass_2
|
pclass_3
|
embarked_C
|
embarked_Q
|
embarked_S
|
is_child
|
fare
|
age
|
survived
|
|
0
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0.0
|
0.0
|
1.0
|
0
|
0.000000
|
0.346569
|
0
|
|
1
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1
|
0.072227
|
0.007288
|
1
|
|
2
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0
|
0.015412
|
0.497361
|
0
|
|
3
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0.0
|
0.0
|
1.0
|
0
|
0.025374
|
0.296306
|
0
|
|
4
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
1
|
0.018543
|
0.195778
|
0
|
{% endraw %}
{% raw %}
|
|
sex_female
|
sex_male
|
pclass_1
|
pclass_2
|
pclass_3
|
embarked_C
|
embarked_Q
|
embarked_S
|
is_child
|
fare
|
age
|
survived
|
|
0
|
1.0
|
0.0
|
0.0
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0
|
0.030254
|
0.346569
|
1
|
|
1
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0
|
0.014151
|
0.271174
|
0
|
|
2
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
1.0
|
0.0
|
0.0
|
0
|
0.007832
|
0.246042
|
0
|
|
3
|
1.0
|
0.0
|
0.0
|
0.0
|
1.0
|
0.0
|
1.0
|
0.0
|
0
|
0.015094
|
0.346569
|
1
|
|
4
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0.0
|
0.0
|
1.0
|
0
|
0.043966
|
0.346569
|
0
|
{% endraw %}
Now let’s train a Logistic Regression model.
We’ll use gridsearch and it will automatically return the best model. We’ll use Stratified K-fold for the Cross Validation technique during grid search.
{% raw %}
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
Gridsearching with the following parameters: {'C': [0.1, 0.5, 1], 'max_iter': [100, 1000]}
Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 0.2s finished
LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=42, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)
{% endraw %}
Once a model is trained a ModelAnalysis object is returned which allows us to analyze, interpret and visualize our model results. Included is a list to help you debug your model if it’s overfit or underfit!
{% raw %}
{% endraw %}
You can quickly cross validate any model by calling cross_validate on the resulting ModelAnalysis object. It will display the mean score across all folds and a learning curve.
For classification problems the default cross validation method is Stratified K-Fold. This allows to maintain some form of class balance, while for regression, the default is K-Fold.
{% raw %}
{% endraw %}
{% raw %}
|
|
log_reg
|
Description
|
|
Accuracy
|
0.780
|
Measures how many observations, both positive and negative, were correctly classified.
|
|
Balanced Accuracy
|
0.774
|
The balanced accuracy in binary and multiclass classification problems to deal with imbalanced datasets. It is defined as the average of recall obtained on each class.
|
|
Average Precision
|
0.822
|
Summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold
|
|
ROC AUC
|
0.853
|
Shows how good at ranking predictions your model is. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.
|
|
Zero One Loss
|
0.220
|
Fraction of misclassifications.
|
|
Precision
|
0.703
|
It measures how many observations predicted as positive are positive. Good to use when False Positives are costly.
|
|
Recall
|
0.744
|
It measures how many observations out of all positive observations have we classified as positive. Good to use when catching call positive occurences, usually at the cost of false positive.
|
|
Matthews Correlation Coefficient
|
0.542
|
It’s a correlation between predicted classes and ground truth.
|
|
Log Loss
|
0.450
|
Difference between ground truth and predicted score for every observation and average those errors over all observations.
|
|
Jaccard
|
0.566
|
Defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of true labels.
|
|
Hinge Loss
|
0.511
|
Computes the average distance between the model and the data using hinge loss, a one-sided metric that considers only prediction errors.
|
|
Hamming Loss
|
0.220
|
The Hamming loss is the fraction of labels that are incorrectly predicted.
|
|
F-Beta
|
0.711
|
It’s the harmonic mean between precision and recall, with an emphasis on one or the other. Takes into account both metrics, good for imbalanced problems (spam, fraud, etc.).
|
|
F1
|
0.723
|
It’s the harmonic mean between precision and recall. Takes into account both metrics, good for imbalanced problems (spam, fraud, etc.).
|
|
Cohen Kappa
|
0.541
|
Cohen Kappa tells you how much better is your model over the random classifier that predicts based on class frequencies. Works well for imbalanced problems.
|
|
Brier Loss
|
0.220
|
It is a measure of how far your predictions lie from the true values. Basically, it is a mean square error in the probability space.
|
{% endraw %}
Lets’s manually train a Logistic Regression and view and verify the results.
{% raw %}
Accuracy: 0.78
AUC: 0.853
Precision: 0.703
{% endraw %}
Similar to Modelling, Aethos 2.0 introduces 4 model analysis objects: ClassificationModelAnalysis, RegressionModelAnalysis, UnsupervisedModelAnalysis and TextModelAnalysis. In Aethos 2.0 they can be initialized in 2 ways:
-
Result of training a model using Aethos
-
Initializing it on your own by providing a Model object, the train data used by the model and the test data to evaluate model performance (for Regression and Classification).
Similar to the Model objects we’re going to explore the ClassificationModelAnalysis object but the process would be the same for regression, unsupervised and text model analysis.
To start, we’ll pick off from where we left off with modelling and view the metrics for our Logistic Regression model.
{% raw %}
aethos.model_analysis.classification_model_analysis.ClassificationModelAnalysis
{% endraw %}
{% raw %}
|
|
log_reg
|
Description
|
|
Accuracy
|
0.780
|
Measures how many observations, both positive and negative, were correctly classified.
|
|
Balanced Accuracy
|
0.774
|
The balanced accuracy in binary and multiclass classification problems to deal with imbalanced datasets. It is defined as the average of recall obtained on each class.
|
|
Average Precision
|
0.822
|
Summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold
|
|
ROC AUC
|
0.853
|
Shows how good at ranking predictions your model is. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.
|
|
Zero One Loss
|
0.220
|
Fraction of misclassifications.
|
|
Precision
|
0.703
|
It measures how many observations predicted as positive are positive. Good to use when False Positives are costly.
|
|
Recall
|
0.744
|
It measures how many observations out of all positive observations have we classified as positive. Good to use when catching call positive occurences, usually at the cost of false positive.
|
|
Matthews Correlation Coefficient
|
0.542
|
It’s a correlation between predicted classes and ground truth.
|
|
Log Loss
|
0.450
|
Difference between ground truth and predicted score for every observation and average those errors over all observations.
|
|
Jaccard
|
0.566
|
Defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of true labels.
|
|
Hinge Loss
|
0.511
|
Computes the average distance between the model and the data using hinge loss, a one-sided metric that considers only prediction errors.
|
|
Hamming Loss
|
0.220
|
The Hamming loss is the fraction of labels that are incorrectly predicted.
|
|
F-Beta
|
0.711
|
It’s the harmonic mean between precision and recall, with an emphasis on one or the other. Takes into account both metrics, good for imbalanced problems (spam, fraud, etc.).
|
|
F1
|
0.723
|
It’s the harmonic mean between precision and recall. Takes into account both metrics, good for imbalanced problems (spam, fraud, etc.).
|
|
Cohen Kappa
|
0.541
|
Cohen Kappa tells you how much better is your model over the random classifier that predicts based on class frequencies. Works well for imbalanced problems.
|
|
Brier Loss
|
0.220
|
It is a measure of how far your predictions lie from the true values. Basically, it is a mean square error in the probability space.
|
{% endraw %}
You can also set project metrics based off your business requirements.
{% raw %}
{% endraw %}
{% raw %}
|
|
log_reg
|
Description
|
|
Accuracy
|
0.780
|
Measures how many observations, both positive and negative, were correctly classified.
|
|
ROC AUC
|
0.853
|
Shows how good at ranking predictions your model is. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.
|
|
Precision
|
0.703
|
It measures how many observations predicted as positive are positive. Good to use when False Positives are costly.
|
{% endraw %}
If you want to just view individual metrics, there are functions for those to!
{% raw %}
{% endraw %}
You can analyze any models results with just one line of code:
-
Metrics
-
Classification Report
-
Confusion Matrix
-
Decision Boundaries
-
Decision Plots
-
Dependence Plots
-
Force Plots
-
LIME Plots
-
Morris Sensitivity
-
Model Weights
-
Summary Plot
-
RoC Curve
-
Individual metrics
And this is only for Classification Models, each type of problem has their own set of ModelAnalysis functions!
{% raw %}
precision recall f1-score support
0 0.83 0.80 0.82 137
1 0.70 0.74 0.72 86
accuracy 0.78 223
macro avg 0.77 0.77 0.77 223
weighted avg 0.78 0.78 0.78 223
{% endraw %}
{% raw %}
{% endraw %}
You can supply features from your train set to the dependency plot otherwise it will just use the first 2 features in your model. Under the hood it uses YellowBricks Decision Boundary visualizer to create the visualizations.
{% raw %}
{% endraw %}
{% raw %}
{% endraw %}
Included are also automated SHAP use cases to interpret your model!
{% raw %}
<shap.plots.decision.DecisionPlotResult at 0x7f521b43abd0>
{% endraw %}
{% raw %}
{% endraw %}
{% raw %}
Visualization omitted, Javascript library not loaded! Have you run initjs() in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
{% endraw %}
{% raw %}
100%|██████████| 111/111 [00:00<00:00, 617.89it/s]
{% endraw %}
View the highest weighted features in your model.
{% raw %}
age : -1.64
sex_male : -1.23
sex_female : 1.23
pclass_3 : -1.06
pclass_1 : 1.05
is_child : 0.56
fare : 0.46
embarked_S : -0.33
embarked_C : 0.20
embarked_Q : 0.13
pclass_2 : 0.00
{% endraw %}
Easily plot an RoC curve.
{% raw %}
<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f521b2e1f90>
{% endraw %}
{% raw %}
{% endraw %}
Finally we can generate the files to deploy our model through a RESTful API using FastAPI, Gunicorn and Docker!
{% raw %}
Deployment files can be found at /home/sidhu/.aethos/projects/aethos2.
To run:
docker build -t `image_name` ./
docker run -d --name `container_name` -p `port_num`:80 `image_name`
{% endraw %}
If we manually trained a model like we did earlier in the notebook and wanted to use Aethos’s model analysis capabilties we can!
{% raw %}
{% endraw %}
{% include note.html content=‘x_train and x_test datasets must have the target variable as part of the DataFrame.’ %}
You will receive the same results as above, thus giving you the ability to manually transform your data, train your model and use Aethos to interpret the results. I’ve included them below for verification.
{% raw %}
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log_reg
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Description
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Accuracy
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0.780
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Measures how many observations, both positive and negative, were correctly classified.
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ROC AUC
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0.853
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Shows how good at ranking predictions your model is. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.
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Precision
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0.703
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It measures how many observations predicted as positive are positive. Good to use when False Positives are costly.
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{% endraw %}
{% raw %}
{% endraw %}
{% raw %}
{% endraw %}
{% raw %}
<shap.plots.decision.DecisionPlotResult at 0x7f521cb2ed10>
{% endraw %}
{% raw %}
{% endraw %}
{% raw %}
Visualization omitted, Javascript library not loaded! Have you run initjs() in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.
{% endraw %}
{% raw %}
100%|██████████| 111/111 [00:00<00:00, 727.94it/s]
{% endraw %}
{% raw %}
age : -1.64
sex_male : -1.23
sex_female : 1.23
pclass_3 : -1.06
pclass_1 : 1.05
is_child : 0.56
fare : 0.46
embarked_S : -0.33
embarked_C : 0.20
embarked_Q : 0.13
pclass_2 : 0.00
{% endraw %}
{% raw %}
<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f521d910c90>
{% endraw %}
{% raw %}
{% endraw %}
{% raw %}
Deployment files can be found at /home/sidhu/.aethos/projects/aethos2.
To run:
docker build -t `image_name` ./
docker run -d --name `container_name` -p `port_num`:80 `image_name`
{% endraw %}
I encourage all feedback about this post or Aethos. You can message me on twitter or e-mail me at sidhuashton@gmail.com.
Any bug or feature requests, please create an issue on the Github repo. I welcome all feature requests and any contributions. This project is a great starter if you’re looking to contribute to an open source project — you can always message me if you need assistance getting started.
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